Computer-based systems configured for automatically updating a database based on an initiation of a dynamic machine-learning verification and methods of use thereof

Abstract

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

What is claimed is: 
     
       1. A computer-implemented method comprising:
 receiving, by at least one processor, input data from at least one external data aggregator at a predetermined interval of time associated with a collection of input data, wherein the input data is telecommunication data; 
 utilizing, by the at least one processor, at least one trained machine learning algorithm to generate a database of known queries associated with a plurality of users based on an analysis of the input data received from the at least one external data aggregator, wherein the analysis of the input data provides additional information associated with a plurality of indicative markers; 
 receiving, by the at least one processor, subsequent input data from the at least one external aggregator at a later interval of time; 
 automatically updating, by the at least one processor, the database of known queries associated with the plurality of users based on the subsequent input data; 
 utilizing, by the at least one processor, the at least one trained machine learning algorithm to perform a cross-reference analysis to determine a presence of at least one data record within the database of known queries, wherein the at least one data record is at least one data point shared between the input data and the subsequent input data; 
 dynamically removing, by the at least one processor, the at least one data record from the database of known queries based on the cross-reference analysis; 
 utilizing, by the at least one processor, the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record comprising the at least one data point, wherein the at least one trigger is a data correctness confidence value associated with the at least one external data aggregator; and 
 instructing, by at least one processor, at least one graphic user interface (GUI) having at least one programmable GUI element within a computing device to initiate a verification of the presence of the at least one data record, comprising the at least one data point, based on the plurality of indicative markers. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein the additional information comprises ownership rights associated with at least one user of the plurality of users. 
     
     
       3. The computer-implemented method of  claim 1 , wherein at least one indicative marker of the plurality of indicative markers comprises a session interaction protocol certificate associated with at least one user of the plurality of users. 
     
     
       4. The computer-implemented method of  claim 1 , wherein the database on known queries comprises a structured query language database. 
     
     
       5. The computer-implemented method of  claim 1 , wherein the data record comprises a phone number associated with at least one user of the plurality of users. 
     
     
       6. The computer-implemented method of  claim 1 , wherein the trigger comprises a frequency associated with a rotation of the plurality of indicative markers to at least one different user of the plurality of users. 
     
     
       7. The computer-implemented method of  claim 1 , further comprising predicting the trigger associated with the presence of the at least one data record to predict a predetermined period of time associated with a subsequent update of the database of known queries based on receiving the subsequent input data. 
     
     
       8. The computer-implemented method of  claim 1 , wherein the at least one external data aggregator comprises a third-party data aggregator associated with collecting telecommunication data. 
     
     
       9. The computer-implemented method of  claim 1 , wherein the data correctness confidence value comprises a calculated value associated with each external data aggregator based on a utilization of a trained crowd sourcing algorithm on social media platform data associated with the plurality of users. 
     
     
       10. The computer-implemented method of  claim 1 , wherein the verification of the presence of the data record comprises, at least one of the following:
 a unique identifier associated with at least one user of the plurality of user verification, 
 a call with at least one agent of a call center verification, and 
 a login portal verification. 
 
     
     
       11. The computer-implemented method of  claim 1 , further comprising automatically restoring the data record within the generated database of known queries in response to a successful verification associated with the data point based on the plurality of indicative markers. 
     
     
       12. A computer-implemented method comprising:
 receiving, by at least one processor, input data from at least one external data aggregator at a predetermined interval of time associated with a collection of input data, wherein the input data is telecommunication data; 
 utilizing, by the at least one processor, at least one trained machine learning algorithm to generate a database of known queries associated with a plurality of users based on an analysis of the input data received from the at least one external data aggregator, wherein the analysis of the input data provides additional information associated with a plurality of indicative markers; 
 receiving, by the at least one processor, subsequent input data from the at least one external aggregator at a later interval of time; 
 automatically updating, by the at least one processor, the database of known queries associated with the plurality of users based on the subsequent input data; 
 utilizing, by the at least one processor, the at least one trained machine learning algorithm to perform a cross-reference analysis to determine a presence of at least one data record within the database of known queries, wherein the at least one data record is at least one data point shared between the input data and the subsequent input data; 
 dynamically removing, by the at least one processor, the at least one data record from the database of known queries based on the cross-reference analysis; 
 utilizing, by the at least one processor, the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record comprising the at least one data point, wherein the at least one trigger is a data correctness confidence value associated with the at least one external data aggregator; 
 instructing, by at least one processor, at least one graphic user interface (GUI) having at least one programmable GUI element within a computing device to initiate a verification of the presence of the at least one data record, comprising the at least one data point, based on the plurality of indicative markers; and 
 automatically restoring the data record within the generated database of known queries in response to a successful verification associated with the data point based on the plurality of indicative markers. 
 
     
     
       13. The computer-implemented method of  claim 12 , wherein at least one indicative marker of the plurality of indicative markers comprises a session interaction protocol certificate associated with at least one user of the plurality of users. 
     
     
       14. The computer-implemented method of  claim 12 , wherein the database on known queries comprises a structured query language database. 
     
     
       15. The computer-implemented method of  claim 12 , wherein the data record comprises a phone number associated with at least one user of the plurality of users. 
     
     
       16. The computer-implemented method of  claim 12 , wherein the trigger comprises a frequency associated with a rotation of the plurality of indicative markers to at least one different user of the plurality of users. 
     
     
       17. The computer-implemented method of  claim 12 , further comprising predicting the trigger associated with the presence of the at least one data record to predict a predetermined period of time associated with a subsequent update of the database of known queries based on receiving the subsequent input data. 
     
     
       18. A system comprising:
 a non-transient computer memory, storing software instructions; 
 at least one processor of a first computing device associated with a user; 
 wherein, when the at least one processor executes the software instructions, the first computing device is programmed to:
 receive, by at least one processor, input data from at least one external data aggregator at a predetermined interval of time associated with a collection of input data, wherein the input data is telecommunication data; 
 utilize, by the at least one processor, at least one trained machine learning algorithm to generate a database of known queries associated with a plurality of users based on an analysis of the input data received from the at least one external data aggregator, wherein the analysis of the input data provides additional information associated with a plurality of indicative markers; 
 receive, by the at least one processor, subsequent input data from the at least one external aggregator at a later interval of time; 
 automatically update, by the at least one processor, the database of known queries associated with the plurality of users based on the subsequent input data; 
 utilize, by the at least one processor, the at least one trained machine learning algorithm to perform a cross-reference analysis to determine a presence of at least one data record within the database of known queries, wherein the at least one data record is at least one data point shared between the input data and the subsequent input data; 
 dynamically remove, by the at least one processor, the at least one data record from the database of known queries based on the cross-reference analysis; 
 utilize, by the at least one processor, the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record comprising the at least one data point, wherein the at least one trigger is a data correctness confidence value associated with the at least one external data aggregator; and 
 instruct, by at least one processor, at least one graphic user interface (GUI) having at least one programmable GUI element within a computing device to initiate a verification of the presence of the at least one data record, comprising the at least one data point, based on the plurality of indicative markers. 
 
 
     
     
       19. The system of  claim 18 , wherein the software instructions further comprise predicting the trigger associated with the presence of the at least one data record to predict a predetermined period of time associated with a subsequent update of the database of known queries based on receiving the subsequent input data. 
     
     
       20. The system of  claim 18 , wherein the software instructions further comprise automatically restoring the data record within the generated database of known queries in response to a successful verification associated with the data point based on the plurality of indicative markers.